What is Chain of Thought?
Chain of Thought (CoT) prompting is a technique where you instruct the AI to show its reasoning step by step before arriving at a final answer. This dramatically improves accuracy on complex tasks like math, logic puzzles, and multi-step analysis.
Why It Works
LLMs generate text token by token. When forced to "think out loud," each reasoning step provides context for the next, reducing errors that occur when the model tries to jump directly to an answer.
How to Use It
Simple CoT: Append "Let's think step by step" to your prompt. This simple phrase can improve accuracy by 10-40% on reasoning tasks.
Structured CoT: Explicitly outline the steps: "First, identify the key variables. Second, establish relationships. Third, calculate the result. Finally, verify your answer."
Few-Shot CoT: Provide an example that demonstrates the reasoning process, then ask the model to follow the same pattern for a new problem.
Best Use Cases
Mathematical word problems, logical deductions, code debugging ("trace through the code step by step"), legal analysis, medical differential diagnosis, and any task requiring multi-step reasoning.
Limitations
CoT adds length to responses, increases token usage, and can be unnecessary for simple factual questions. Use it selectively for tasks where reasoning quality matters.